This map service provides access to scanned as-built drawings from the Kentucky Transportation Cabinet (KYTC). These drawings, dating from 1909 to the present, are linked to geographic locations across Kentucky. They are valuable for historical research, infrastructure planning, and project reference. Users can explore detailed construction records and view changes in the transportation network over time.

Empty geodatabase schema for GIS as-built submissions of new gathering pipeline or natural gas gathering system as defined in 19.15.28.9 NMAC.“Natural gas gathering system” means the gathering pipelines and associated facilities that compress, dehydrate, or treat natural gas after the custody transfer point and ending at the connection point with a natural gas processing plant or transmission or distribution system. 19.15.28.7 NMAC.“Gathering pipeline” means a pipeline that gathers natural gas within a natural gas gathering system. 19.15.28.7 NMAC.“Release” No later than July 1st of each year, the operator shall also file with the division an updated system map GIS digitally formatted as-built map of its gathering pipeline or natural gas gathering system, which shall include a GIS layer that identifies the date, location and volume of vented or flared natural gas of each emergency, malfunction and release reported to the division since 19.15.28 NMAC became applicable to the pipeline or system. System Maps will be submitted to OCD in the Esri file geodatabase format.Do not submit Esri shapefile, personal geodatabase, or other raw formats. Do not submit GIS files that were converted to a file geodatabase format without following the required database template.File Geodatabase and feature layers must use an underscore, rather than a period or space, when naming files. (ex. FacID_Date_NGGS)

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

The Community Map (WGS84) (World Edition) web map provides a customized world basemap that is uniquely symbolized and optimized to display special areas of interest (AOIs) that have been created and edited by Community Maps contributors. These special areas of interest include landscaping features such as grass, trees, and sports amenities like tennis courts, football and baseball field lines, and more. This basemap, included in the ArcGIS Living Atlas of the World, uses the Community (WGS84) vector tile layer.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps WGS84 are updated quarterly.Check out other WGS84 basemaps in the World Basemaps (WGS84) group. Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the layer referenced in this map.Precise Tile Registration The map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

This is a data set of built environment policy instruments relevant to the state of Victoria, Australia. The instruments are categorised against a built environment policy framework developed by Hurlimann et al (2024 - see reference details below) which consists of: a policy instrument typology (strategies, laws, regulations, guidelines, voluntary instruments and programs), and a built environment policy setting (governance level, sector, property type, life stage, timeframe). Local (City of Melbourne), national (Australia) and international built environment policy instruments are also included.This data file relates to open access journal article: Hurlimann, A., March, A., Bush, J., Moosavi., S., Browne, G., Warren-Myers, G., (2024) 'Climate change transformation in built environments - A policy instrument framework. Urban Climate; doi.org/10.1016/j.uclim.2023.101771

This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

The Mid-Century Map (World Edition) web map provides a customized world basemap symbolized with a unique "Mid-Century" style. It takes its inspiration from the art and advertising of the 1950's with unique fonts. The symbols for cities and capitals have an atomic slant to them. The map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries.This basemap, included in the ArcGIS Living Atlas of the World, uses the Mid-Century vector tile layer.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer referenced in this map.

This viewport layer is part of the StreetCar Inventory Map which was created for internal evaluation of bike crossing facilities with Street-Car Tracks. The Plan Set is N-2009-001. Also, As-Built can be found at https://www.tucsonaz.gov/apps/maps-and-records/construction-plans by including the plan number.The focus is specifically on bike legends, green pre-formed areas, and bike lane markings. This will also help establish inventory for striping.Layer Template for point location for pavement markings and signs around streetcar. Simple form to capture direction of travel intended for feature; location in respects to the direction of travel to make intended feature work towards safety; and able to update template to include further detailed information if needed.Project plans are N-2009-001; As Builts are available at https://www.tucsonaz.gov/apps/maps-and-records/construction-plans . Please type in the plan number as see current as-builts in plan database.One layer created along with features, inventory, and domains within the database. Layer created and copied. Two queries created to streamline the data shown but all data points to one complete layer. Please contact fabian.shenk@tucsonaz.gov with any questions or concerns about map, legend, and/or attribute fields.

This National Geographic Style Map (World Edition) web map provides a reference map for the world that includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings, and landmarks, overlaid on shaded relief and a colorized physical ecosystems base for added context to conservation and biodiversity topics. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri, National Geographic or any governing authority.This basemap, included in the ArcGIS Living Atlas of the World, uses the National Geographic Style vector tile layer and the National Geographic Style Base and World Hillshade raster tile layers.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

The 3D Terrestrial Laser Scanning (TLS) equipment market, valued at $2,529 million in 2025, is projected to experience robust growth, driven by a Compound Annual Growth Rate (CAGR) of 6.9% from 2025 to 2033. This expansion is fueled by several key factors. Increasing demand across diverse sectors like oil & gas, mining, and infrastructure for precise 3D data acquisition is a major catalyst. The construction industry's increasing adoption of Building Information Modeling (BIM) and the need for as-built documentation further bolster market growth. Advancements in sensor technology, leading to improved accuracy, range, and speed of data capture, are also significant drivers. Furthermore, the rising availability of user-friendly software solutions for processing and analyzing TLS data is simplifying workflows and broadening market accessibility. The market is segmented by application (Oil & Gas, Mining, Infrastructure, Forestry & Agriculture, Others) and by maximum measuring distance ( <500m, 500-1000m, >1000m), reflecting the varied needs of different user groups. Competition is intense, with key players like Hexagon Geosystems, Trimble, and others constantly innovating to offer superior solutions. Geographical expansion, particularly in developing economies undergoing rapid infrastructure development, presents lucrative opportunities for growth. While the market demonstrates significant growth potential, some challenges persist. High initial investment costs associated with acquiring TLS equipment can be a barrier to entry for smaller firms. The need for skilled professionals to operate and interpret the data also presents a limitation. However, ongoing technological advancements, such as the integration of artificial intelligence (AI) for automated data processing, are progressively addressing these issues. The increasing affordability and accessibility of TLS equipment, coupled with the growing awareness of its benefits, are expected to mitigate these restraints in the long term, contributing to sustained market expansion throughout the forecast period. The increasing demand for precision and efficiency across multiple sectors continues to solidify the position of 3D terrestrial laser scanning as an indispensable technology for a range of applications.

This dataset comprises road centerlines for all roads in San Diego County. Road centerline information is collected from recorded documents (subdivision and parcel maps) and information provided by local jurisidictions (Cities in San Diego County, County of San Diego). Road names and address ranges are as designated by the official address coordinator for each jurisidcition. Jurisdictional information is created from spatial overlays with other data layers (e.g. Jurisdiction, Census Tract).The layer contains both public and private roads. Not all roads are shown on official, recorded documents. Centerlines may be included for dedicated public roads even if they have not been constructed. Public road names are the official names as maintained by the addressing authority for the jurisdiction in which the road is located. Official road names may not match the common or local name used to identify the road (e.g. State Route 94 is the official name of certain road segments commonly referred to as Campo Road).Private roads are either named or unnamed. Named private roads are as shown on official recorded documents or as directed by the addressing authority for the jurisdiction in which the road is located. Unnamed private roads are included where requested by the local jurisidiction or by SanGIS JPA members (primarily emergency response dispatch agencies). Roads are comprised of road segments that are individually identified by a unique, and persistent, ID (ROADSEGID). Roads segments are terminated where they intersect with each other, at jurisdictional boundaries (i.e. city limits), certain census tract and law beat boundaries, at locations where road names change, and at other locations as required by SanGIS JPA members. Each road segment terminates at an intersection point that can be found in the ROADS_INTERSECTION layer.Road centerlines do not necessarily follow the centerline of dedicated rights-of-way (ROW). Centerlines are adjusted as needed to fit the actual, constructed roadway. However, many road centerline segments are created intially based on record documents prior to construction and may not have been updated to meet as-built locations. Please notify SanGIS if the actual location differs from that shown. See the SanGIS website for contact information and reporting problems (http://www.sangis.org/contact/problem.html).Note, the road speeds in this layer are based on road segment class and were published as part of an agreement between San Diego Fire-Rescue, the San Diego County Sheriff's Department, and SanGIS. The average speed is based on heavy fire vehicles and may not represent the posted speed limit.

This Feature Class was created in 2014 as part of a Connecticut Office of Policy and Management/The original parcel layer was digitized from tax maps originally created by General Mapping , Inc. of Youngwood, PA in 1970, last revised in 1999. These maps were drawn over unrectified aerial photos, resulting in significant distortion. More current orthophotos, specifically those from the 2009 CCROG flight and the 2012 State flight were used to refine the property lines using obvious indications of property lines such as fences and hedgerows. In addition, over 100 parcels were drawn by Coordinate Geography (COGO) using as-built maps. These points are used for assigning attributes to parcel polygons.Condominiums are drawn as individual polygons with their own points.updated April 2018

The Charted Territory Map (World Edition) web map provides a customized world basemap uniquely symbolized. It takes its inspiration from a printed atlas plate and pull-down scholastic classroom maps. The map emphasizes the geographic and political features in the design. The use of country level polygons are preassigned with eight different colors. It also includes the global graticule features as well as landform labels of physical features and hillshade. This basemap, included in the ArcGIS Living Atlas of the World, uses the Charted Territory vector tile layer and World Hillshade. The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the layers referenced in this map.

Data can be viewed interactively here: https://nina.earthengine.app/view/nedbygging

(see Norwegian description below)

1. Dataset Information

- Title: Map of built-up expansion over Norway 2017-2022

- Author(s): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)

- Contact Information: zander.venter@nina.no

- Date of Data Generation: 06.01.2024

- Version: 1

- Description: This is the dataset used in the NRK article published on 06.01.2024. The data contains polygons outlining potential “nedbygging” (hereafter translated to “built-up expansion” in English) events between 2017 and 2022 over Norway. The built-up expansion polygons were identified using a combination of Sentinel-2 satellite imagery, a fully convolutional neural network (a type of AI model) from Google called Dynamic World and NINA’s time series analysis thereof. The method to create the map will be published by NINA at a later date. The original map was created by NINA, but NRK performed some post-processing which included joining some polygons which were part of the same built-up expansion event (e.g. a long road). It is important to note that the map is a result of AI and has errors in it. Therefore, users are encouraged to read the sections on data quality and usage information below. Users can refer to Venter et al. (2024) for details on the scientific best practice which the NRK journalists followed to ensure that their reported area estimates in the article were not biased. In summary, the map is wrong 18% of the time. Users should expect to find that on average 1 in 5 square meter is incorrectly identified as built-up expansion. There are also many instances of built-up expansion which will be missed in the map such as forestry road development, building of small cabins etc.

2. File Details

- Format: Shapefile (.shp, .shx, .dbf, .prj)

- Size: 13.27 MB

3. Geospatial Information

- Coordinate System: EPSG:32632, UTM zone 32N

- Spatial Resolution: 10m

- Geographical Coverage: Norway mainland (excludes Svalbard)

- Temporal Coverage: 2017 to 2022

4. Data Content

- Attributes Included:

- *id*: unique identity number for each polygon

- *undersøkt*: whether the polygon has been investigated manually using visual interpretation of orthophotos. “ja” = “yes” and “nei” = “no”

- *ai_feil*: whether the AI model method correctly (“riktig”) or incorrectly (“feil”) identified natural habitat conversion to built-up surface. Values where *undersøkt* == “nei” are labelled as “ikke_verifisert”

5. Data Quality

- Accuracy: As described above, the false positive rate of the map was 18% based on 500 locations used for map validation and accuracy assessment. We did not quantify a false negative rate and balanced accuracy estimates because this would have required a denser sample for manual verification. Therefore, it is likely that there are many instances of built-up expansion that our map does not capture. After the formal accuracy assessment using the 500 stratified random points, NRK verified additional polygons (total of 3875) in the dataset during their investigative journalism workflow. Although these were not collected in a systematic manner, then can still be useful for some downstream tasks such as exploring what causes the AI model to misidentify built-up expansion.

- Validation Methods: A design-based approach was used to quantify map accuracy and estimate uncertainty around the resulting area estimate reported in the NRK article. The details of this method are reported in Venter et al. (2024). This approach quantifies the error in the AI-derived map, and corrects for this using a stratified area estimator. Therefore, the total built-up expansion of 208 km<2> reported in the NRK article has been bias-corrected. We also quantified 95% confidence intervals around this are estimate of 9.8 km<2>. It is important to note that the validation approach was conducted on individual Sentinel-2 pixels of 10x10m and not at the polygon level. Therefore, we did not quantify the error in the precision of the polygon shape in terms of capturing the full extent of a given built-up expansion event.

6. Usage Information

- Use Limitations: Considering the map error described above, users should proceed with caution when analysing the map to derive area statistics or overlays with other maps. As described in Venter et al. (2024), simply adding the areas of the polygons (or “pixel counting” with maps formatted as images) without accounting for the error in the map will lead to incorrect area statistics. We recommend that users validate the map for their municipality or study area before proceeding with analysis. It is likely that the margin of error is highly variable between municipalities. For example, although we have not quantified it, we noticed many AI mistakes in mountainous regions due to snow and ice interference and therefore high-altitude municipalities might have more errors than low-altitude ones.

Norwegian description:

1. Datasettinformasjon

- Tittel: Kart over nedbygging over Norge 2017-2022

- Forfatter(e): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)

- Kontaktinformasjon: zander.venter@nina.no

- Dato for datagenerering: 06.01.2024

- Versjon: 1

- Beskrivelse: Dette er datasettet som brukes i NRK-artikkelen publisert 06.01.2024. Dataene inneholder polygoner som skisserer potensiell nedbygging mellom 2017 og 2022 over Norge. Nedbyggingsområdene ble identifisert ved hjelp av en kombinasjon av Sentinel-2 satellittbilder, et fullstendig konvolusjonelt nevralt nettverk (en type KI-modell) fra Google kalt Dynamic World og NINAs tidsserie-analyse av dette. Metoden for å lage kartet vil bli publisert av NINA på et senere tidspunkt. Det originale kartet ble laget av NINA, men NRK utførte en del etterbehandling som inkluderte sammenføyning av noen polygoner som var en del av den samme oppbygde utvidelseshendelsen (f.eks. en lang vei). Det er viktig å merke seg at kartet er produsert ved hjelp av kunstig intelligens og inneholder feil. Derfor oppfordres brukere til å lese avsnittene om datakvalitet og bruksinformasjon nedenfor. Brukere kan referere til Venter et al. (2024) for detaljer om den vitenskapelige beste praksisen som NRK-journalistene fulgte for å sikre at deres rapporterte arealstatistikk i artikkelen er korrekt. Oppsummert er 18 % av arealet i kartet feil. Brukere bør forvente å finne at i gjennomsnitt 1 av 5 kvadratmeter er feilaktig identifisert som nedbygging. Det er også mange tilfeller av nedbygging som som ikke vil vises i kartet, som skogsveiutbygging, bygging av småhytter mm.

2. Fildetaljer

- Format: Shapefil (.shp, .shx, .dbf, .prj)

- Størrelse: 13,27 MB

3. Geospatial informasjon

- Koordinatsystem: EPSG:32632, UTM-sone 32N

- Rolig oppløsning: 10m

- Geografisk dekning: Norges fastland (ekskluderer Svalbard)

- Tidlig dekning: 2017 til 2022

4. Datainnhold

- Attributter inkludert:

- *id*: unikt identitetsnummer for hver polygon

- *undersøkt*: om polygonet er undersøkt manuelt ved bruk av visuell tolkning av ortofoto.

- *ai_feil*: om AI-modellmetoden var “riktig” eller “feil”. Verdier der *undersøkt* == «nei» er merket som «ikke_verifisert»

5. Datakvalitet

- Nøyaktighet: Som beskrevet ovenfor var andelen falske positive punkter i kartet 18 % basert på 500 steder (prøveflater) brukt for kartvalidering og nøyaktighetsvurdering. Vi kvantifiserte ikke andelen falske negative punkter og balanserte nøyaktighetsestimater, fordi dette ville ha krevd en tettere stikkprøvedensitet for manuell verifisering. Derfor er det sannsynlig at det er mange tilfeller av nedbygging som kartet vårt ikke fanger opp. Etter den formelle nøyaktighetsvurderingen ved bruk av 500 stratifiserte tilfeldige prøveflater, verifiserte NRK ytterligere polygoner (totalt 3875) i datasettet i løpet av deres journalistiske undersøkelser. Selv om disse ikke ble samlet inn på en systematisk måte, kan de fortsatt være nyttige for noen oppfølgingsanalyser som å utforske hva som får AI-modellen til å feilidentifisere nedbygging.

- Valideringsmetoder: En designbasert tilnærming («design-based area estimation» på engelsk) ble brukt for å kvantifisere kartnøyaktighet og estimere usikkerhet rundt det resulterende arealestimatet rapportert i NRK-artikkelen. Detaljene ved denne metoden er forklart i Venter et al. (2024). Denne tilnærmingen kvantifiserer feilen i det KI-avledede kartet, og korrigerer for dette ved å bruke en stratifisert arealestimator. Derfor er den

The Modern Antique Map (World Edition) web map provides a world basemap symbolized with a unique antique styled map, with a modern flair -- including the benefit of multi-scale mapping. The comprehensive map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. This basemap, included in the ArcGIS Living Atlas of the World, uses the Modern Antique vector tile layer and World Hillshade.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Clean outs are a type of asset that allow access for maintenance purposes to smaller sewer lines which includes both main lines and laterals. Operations staff can use this layer to easily determine where cleaning of some sections of gravity based collections systems will not be possible with their primary equipment and to adjust accordingly. Locations are derived from as-builts and coordination with field staff.Attribute Information:Field Name DescriptionOBJECTIDESRI software specific field that serves as an index for the database.FacilityIDA unique identifier for the asset class. Infor required field.LocationDescriptionInformation related to the construction location or project name. Infor required fieldCommentsA catch all for asset information that is irregular and doesn't warrant the creation of a new field.LastUpdateDate when asset was most recently updated.LastEditorName of user whom most recently edited asset information.EnabledESRI software specific field related to the inclusion in a network.AncillaryRoleESRI software specific field related to the role played within a network.GlobalIDESRI software specific field that is automatically assigned by the geodatabase at row creation.ShapeESRI software specific field denoting the geometry type of the asset.created_userName of user whom created the asset.created_dateDate when the asset was created.last_edited_userName of user whom most recently edited asset information.last_edited_dateDate when asset was most recently updated.IsLocatedHas the location of the asset been field verified with a survey grade GPS unit?InstallDateThe date when the asset was installed. Typically pulled from the as-built cover sheet for consistency. Infor required field.LifecycleStatusThe current status of the asset with respect to its location in the asset management lifecycle. Infor required field.

Scanned Hardcopy Maps dataset current as of 2010. Various scans of historic tax maps, stormwater and utility as-builts, plats, etc..

Current features defined by the Subtype field are: CathodicProtectionTestPoint, FlowMeter, and PumpStation. Features are sourced from as-built drawings and field verified with a survey grade GPS unit when appropriate.Attribute Information: Field Name Description

Name The common name for the facility.

AssetID A unique identifier for the asset class. Infor required field.

InstallDate The date when the asset was installed. Typically pulled from the as-built cover sheet for consistency. Infor required field.

LifecycleStatus The current status of the asset with respect to its location in the asset management lifecycle. Infor required field.